class_docstr: RMS Normalization layer along the last dimension.
  
  This is similar to torch.nn.functional.normalize but with eps being added
  instead of max.
  
  Expects contiguous input of shape (..., dim), and returns normalized data
  of the same shape. For each dim-length vector x, the result has
  
      x / sqrt( x*x.sum() + eps)
  
  If weights are included, they are a parameter of length dim which multiplies
  the result.
  
  This functionality is experimental. Its API might be changed without warnings.
  Use it at your own risk.
docstr: An addition fused with forward.
  
      z = layer.increment_and_forward_(x, y)
  
  is equivalent to
  
      x += y
      z = layer(x)
docstr_tok: ["An","addition","fused","with","forward",".","z","=","layer.increment_and_forward_","(","x",",","y",")","is","equivalent","to","x","+=","y","z","=","layer","(","x",")"]
code: def increment_and_forward_(self, x: torch.Tensor, y: torch.Tensor):
      """
      An addition fused with forward.
  
          z = layer.increment_and_forward_(x, y)
  
      is equivalent to
  
          x += y
          z = layer(x)
      """
      return rms_norm_add(x, y, self.weight, self.eps)
  
file_path: xformers/xformers/ops/rmsnorm.py
imports: from typing import Optional
  import torch
  from torch import nn
  from ..None import _is_triton_available
label_desc: Use image node_id 3 for calling the RMSNorm obj's underlying member method code with example usage: obj.increment_and_forward_(x, y) and returns: rms_norm_add